Optimizing Aerospace Missions with Convolutional Neural Networks: Innovations and Advantages

Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have brought transformative innovations to the aerospace industry. The aim of this paper is to demonstrate the innovative applications of CNNs in aerospace and their performance advantages in complex tasks. A detailed analysis of CNNs' specific benefits for aerospace missions is conducted by juxtaposing them with traditional algorithms and synthesizing existing research to delineate their principal application domains. Furthermore, this paper proposes a CNN-based visual information processing methodology designed to enhance data processing capabilities in aerospace contexts. The results show that CNNs have significant advantages in areas such as attitude control, visual navigation, remote sensing image analysis, fault diagnosis and condition monitoring, human-computer interaction and health monitoring. Notably, CNNs outperform traditional algorithms in complex scene feature extraction, significantly improving the accuracy and efficiency of tasks. These results indicate that CNNs have great potential for application in aerospace missions and are expected to further promote the development of the aerospace field and deepen their application in complex mission scenarios.